GlobalAveragePooling2D層
keras.layers.pooling.GlobalAveragePooling2D(dim_ordering=‘default‘)
為空域信號施加全局平均值池化
參數
- data_format:字符串,“channels_first”或“channels_last”之一,代表圖像的通道維的位置。該參數是Keras 1.x中的image_dim_ordering,“channels_last”對應原本的“tf”,“channels_first”對應原本的“th”。以128x128的RGB圖像為例,“channels_first”應將數據組織為(3,128,128),而“channels_last”應將數據組織為(128,128,3)。該參數的默認值是
~/.keras/keras.json
中設置的值,若從未設置過,則為“channels_last”。
輸入shape
‘channels_first’模式下,為形如(samples,channels, rows,cols)的4D張量
‘channels_last’模式下,為形如(samples,rows, cols,channels)的4D張量
輸出shape
形如(nb_samples, channels)的2D張量

示例代碼
keras-finetuning
def build_model(nb_classes): base_model = InceptionV3(weights='imagenet', include_top=False) # add a global spatial average pooling layer x = base_model.output x = GlobalAveragePooling2D()(x) # let's add a fully-connected layer x = Dense(1024, activation='relu')(x) # and a logistic layer predictions = Dense(nb_classes, activation='softmax')(x) # this is the model we will train model = Model(input=base_model.input, output=predictions) # first: train only the top layers (which were randomly initialized) # i.e. freeze all convolutional InceptionV3 layers for layer in base_model.layers: layer.trainable = False # compile the model (should be done *after* setting layers to non-trainable) print "starting model compile" compile(model) print "model compile done" return model
Kaggle-Sea-Lions-Solution
def get_model(): input_shape = (image_size, image_size, 3) model = Sequential() model.add(Conv2D(32, kernel_size=(3, 3), padding='same', input_shape=input_shape)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(n_classes, kernel_size=(3, 3), padding='same')) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(GlobalAveragePooling2D()) print (model.summary()) #sys.exit(0) # model.compile(loss=keras.losses.mean_squared_error, optimizer= keras.optimizers.Adadelta()) return model